System and method for partition-scoped snapshot creation in a distributed data computing environment

ABSTRACT

A system and method for partitioned snapshot creation of caches in a distributed data grid is provided. The system and method enables a snapshot to be created in a running system without quiescing a cache service. Moreover for each particular partition, execution of read/write requests are not blocked during the period that a snapshot creation task is being performed for the particular partition. The cache service thread continues to execute read requests for all partitions with write requests for the partition under snapshot experiencing delayed response. The system and method reduces the period of time for which partitions are unavailable during a snapshot process and increases the availability of cache services provided by a distributed data grid compared to prior snapshot systems.

CLAIM OF PRIORITY

This application claims the benefit of priority to U.S. ProvisionalApplication No. 62/491,706 filed Apr. 28, 2017 titled “SYSTEM AND METHODFOR PARTITION-SCOPED SNAPSHOT CREATION IN A DISTRIBUTED DATA COMPUTINGENVIRONMENT” which application is herein incorporated by reference inits entirety

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialwhich is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent file or records, but otherwise reserves all copyrightrights whatsoever.

FIELD OF INVENTION

The present invention is generally related to computer systems, and isparticularly related to a distributed data grid.

SUMMARY

Described herein are systems and methods that can supportpartition-scoped snapshot creation of caches in a distributed data gridis provided. The system and method enables a snapshot to be created in arunning system without quiescing/suspending a cache service. Moreoverfor each particular partition, execution of write requests is onlyblocked during the period that a partition-scoped snapshot creation taskis being performed for the particular partition. The cache service isnot suspended, and continues to execute read/write requests for anypartition that is not currently undergoing a snapshot process. Thesystem and method reduces the period of time for which partitions areunavailable during a snapshot process and increases the availability ofcache services provided by a distributed data grid compared to priorsnapshot systems.

These and other objects and advantages of the present invention willbecome apparent to those skilled in the art from the followingdescription of the various embodiments, when read in light of theaccompanying drawings.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 illustrates a distributed data grid, in accordance with anembodiment of the invention.

FIG. 2 illustrates a method for partitioned snapshot creation in adistributed data grid, in accordance with an embodiment of theinvention.

FIG. 3 illustrates an implementation of the system and method forpartitioned snapshot creation using an association pile in accordancewith an embodiment of the invention.

FIGS. 4A and 4B illustrate an implementation of the system and methodfor partitioned snapshot creation using a scalable association pile inaccordance with an embodiment of the invention.

DETAILED DESCRIPTION

Described herein are systems and methods that can supportpartition-scoped snapshot creation of caches in a distributed data grid.The system and methods for partition-scoped snapshot creation providedfor adaptive incremental creation of partition-scoped distributedsnapshots. The system and method enables a snapshot to be created in arunning system without quiescing a cache service. Moreover for eachparticular partition, execution of read/write requests is only blockedduring the period that a snapshot creation task is being performed forthe particular partition. The cache service can therefore continue toexecute read/write requests for any partition that is not currentlyundergoing a snapshot process. The system and method reduces the periodof time for which partitions are unavailable during a snapshot processand increases the availability of cache services provided by adistributed data grid compared to prior snapshot systems. The system andmethods for providing partitioned snapshot creation as described hereinhave particular utility in the distributed data grid described belowwith respect to FIG. 1. The scalable thread pool disclosed herein mayalso be applied in wide variety of multi-threaded processingenvironments and applications.

In the following description, the invention will be illustrated by wayof example and not by way of limitation in the figures of theaccompanying drawings. References to various embodiments in thisdisclosure are not necessarily to the same embodiment, and suchreferences mean at least one. While specific implementations arediscussed, it is understood that this is provided for illustrativepurposes only. A person skilled in the relevant art will recognize thatother components and configurations may be used without departing fromthe scope and spirit of the invention.

Furthermore, in certain instances, numerous specific details will be setforth to provide a thorough description of the invention. However, itwill be apparent to those skilled in the art that the invention may bepracticed without these specific details. In other instances, well-knownfeatures have not been described in as much detail so as not to obscurethe invention.

The present invention is described with the aid of functional buildingblocks illustrating the performance of specified functions andrelationships thereof. The boundaries of these functional buildingblocks have often been arbitrarily defined herein for the convenience ofthe description. Thus functions shown to be performed by the sameelements may in alternative embodiments be performed by differentelements. And functions shown to be performed in separate elements mayinstead be combined into one element. Alternate boundaries can bedefined so long as the specified functions and relationships thereof areappropriately performed. Any such alternate boundaries are thus withinthe scope and spirit of the invention.

Common reference numerals are used to indicate like elements throughoutthe drawings and detailed description; therefore, reference numeralsused in a figure may or may not be referenced in the detaileddescription specific to such figure if the element is describedelsewhere. The first digit in a three digit reference numeral indicatesthe series of figures in which the element first appears.

Distributed Data Grid

A distributed data grid is a system in which a collection of computerservers work together in one or more clusters to manage information andrelated operations, such as computations, within a distributed orclustered environment. A distributed data grid can be used to manageapplication objects and data that are shared across the servers. Adistributed data grid provides low response time, high throughput,predictable scalability, continuous availability and informationreliability. As a result of these capabilities, a distributed data gridis well suited for use in computational intensive, stateful middle-tierapplications. In particular examples, distributed data grids, such ase.g., the Oracle® Coherence data grid, store information in-memory toachieve higher performance, and employ redundancy in keeping copies ofthat information synchronized across multiple servers, thus ensuringresiliency of the system and continued availability of the data in theevent of failure of a server.

In the following description, an Oracle® Coherence data grid having apartitioned cache is described. However, one of ordinary skill in theart will understand that the present invention, described for example inthe summary above, can be applied to any distributed data grid known inthe art without departing from the scope of the invention. Moreover,although numerous specific details of an Oracle® Coherence distributeddata grid are described to provide a thorough description of theinvention, it will be apparent to those skilled in the art that theinvention may be practiced in a distributed data grid without thesespecific details. Thus, a particular implementation of a distributeddata grid embodying the present invention can, in some embodiments,exclude certain features, and/or include different, or modified featuresthan those of the distributed data grid described below, withoutdeparting from the scope of the invention.

FIG. 1 illustrates and example of a distributed data grid 100 whichstores data and provides data access to clients 150. A “data gridcluster”, or “distributed data grid”, is a system comprising a pluralityof computer servers (e.g., 120 a, 120 b, 120 c, and 120 d) which worktogether in one or more cluster (e.g., 100 a, 100 b, 100 c) to store andmanage information and related operations, such as computations, withina distributed or clustered environment. While distributed data grid 100is illustrated as comprising four servers 120 a, 120 b, 120 c, 120 d,with five data nodes 130 a, 130 b, 130 c, 130 d, and 130 e in a cluster100 a, the distributed data grid 100 may comprise any number of clustersand any number of servers and/or nodes in each cluster. The distributeddata grid can store the information in-memory to achieve higherperformance, and employ redundancy in keeping copies of that informationsynchronized across multiple servers, thus ensuring resiliency of thesystem and continued availability of the data in the event of serverfailure. In an embodiment, the distributed data grid 100 implements thepresent invention, described for example in the summary above and thedetailed description below.

As illustrated in FIG. 1, a distributed data grid provides data storageand management capabilities by distributing data over a number ofservers (e.g., 120 a, 120 b, 120 c, and 120 d) working together. Eachserver of the data grid cluster may be a conventional computer systemsuch as, for example, a “commodity x86” server hardware platform withone to two processor sockets and two to four CPU cores per processorsocket. Each server (e.g., 120 a, 120 b, 120 c, and 120 d) is configuredwith one or more CPU, Network Interface Card (NIC), and memoryincluding, for example, a minimum of 4 GB of RAM up to 64 GB of RAM ormore. Server 120 a is illustrated as having CPU 122 a, Memory 124 a andNIC 126 a (these elements are also present but not shown in the otherServers 120 b, 120 c, 120 d). Optionally each server may also beprovided with flash memory—e.g. SSD 128 a—to provide spillover storagecapacity. When provided the SSD capacity is preferably ten times thesize of the RAM. The servers (e.g., 120 a, 120 b, 120 c, 120 d) in adata grid cluster 100 a are connected using high bandwidth NICs (e.g.,PCI-X or PCIe) to a high-performance network switch 120 (for example,gigabit Ethernet or better).

A cluster 100 a preferably contains a minimum of four physical serversto avoid the possibility of data loss during a failure, but a typicalinstallation has many more servers Failover and failback are moreefficient the more servers that are present in each cluster and theimpact of a server failure on a cluster is lessened. To minimizecommunication time between servers, each data grid cluster is ideallyconfined to a single switch 102 which provides single hop communicationbetween servers. A cluster may thus be limited by the number of ports onthe switch 102. A typical cluster will therefore include between 4 and96 physical servers.

In most Wide Area Network (WAN) configurations of a distributed datagrid 100, each data center in the WAN has independent, butinterconnected, data grid clusters (e.g., 100 a, 100 b, and 100 c). AWAN may, for example, include many more clusters than shown in FIG. 1.Additionally, by using interconnected but independent clusters (e.g.,100 a, 100 b, 100 c) and/or locating interconnected, but independent,clusters in data centers that are remote from one another, thedistributed data grid can secure data and service to clients 150 againstsimultaneous loss of all servers in one cluster caused by a naturaldisaster, fire, flooding, extended power loss and the like. Clustersmaintained throughout the enterprise and across geographies constitutean automatic ‘backup store’ and high availability service for enterprisedata.

One or more nodes (e.g., 130 a, 130 b, 130 c, 130 d and 130 e) operateon each server (e.g., 120 a, 120 b, 120 c, 120 d) of a cluster 100 a. Ina distributed data grid the nodes may be for example, softwareapplications, virtual machines, or the like and the servers may comprisean operating system, hypervisor or the like (not shown) on which thenode operates. In an Oracle® Coherence data grid, each node is Javavirtual machine (JVM). A number of JVM/nodes may be provided on eachserver depending on the CPU processing power and memory available on theserver. JVM/nodes may be added, started, stopped, and deleted asrequired by the distributed data grid. JVMs that run Oracle® Coherenceautomatically join and cluster when started. JVM/nodes that join acluster are called cluster members or cluster nodes.

In an Oracle® Coherence data grid cluster members communicate usingTangosol Cluster Management Protocol (TCMP). TCMP is an IP-basedprotocol that is used to discover cluster members, manage the cluster,provision services, and transmit data between cluster members. The TCMPprotocol provides fully reliable, in-order delivery of all messages.Since the underlying UDP/IP protocol does not provide for eitherreliable or in-order delivery, TCMP uses a queued, fully asynchronousACK and NACK-based mechanism for reliable delivery of messages, withunique integral identity for guaranteed ordering of messages in queuesassociated with the JVMs operating on a server. The TCMP protocolrequires only three UDP/IP sockets (one multicast, two unicast) and sixthreads per JVM/node, regardless of the cluster size.

The functionality of a data grid cluster is based on services providedby cluster nodes. Each service provided by a cluster node has a specificfunction. Each cluster node can participate in (be a member of) a numberof cluster services, both in terms of providing and consuming thecluster services. Some cluster services are provided by all nodes in thecluster whereas other services are provided by only one or only some ofthe nodes in a cluster. Each service has a service name that uniquelyidentifies the service within the data grid cluster, and a service type,which defines what the service can do. There may be multiple namedinstances of each service type provided by nodes in the data gridcluster (other than the root cluster service). All services preferablyprovide failover and failback without any data loss.

Each service instance provided by a cluster node typically uses oneservice thread to provide the specific functionality of the service. Forexample, a distributed cache service provided by a node is provided bysingle service thread of the node. When the schema definition for thedistributed cache is parsed in the JVM/node, a service thread isinstantiated with the name specified in the schema. This service threadmanages the data in the cache created using the schema definition. Someservices optionally support a thread pool of worker threads that can beconfigured to provide the service thread with additional processingresources. The service thread cooperates with the worker threads in thethread pool to provide the specific functionality of the service.

In an Oracle® Coherence data grid, the cluster service (e.g., 136 a, 136b, 136 c, 136 d, 136 e) keeps track of the membership and services inthe cluster. Each cluster node always has exactly one service of thistype running. The cluster service is automatically started to enable acluster node to join the cluster. The cluster service is responsible forthe detection of other cluster nodes, for detecting the failure (death)of a cluster node, and for registering the availability of otherservices in the cluster. The proxy service (e.g., 138 c) allowsconnections (e.g. using TCP) from clients that run outside the cluster.The invocation Service (e.g., 134 d) allows application code to invokeagents to perform operations on any node in the cluster, or any group ofnodes, or across the entire cluster. Although shown on only one nodeeach, the invocation service and proxy service can be configured on anynumber up to all of the nodes of the distributed data grid. Agentsallows for execution of code/functions on nodes of the distributed datagrid (typically the same node as data required for execution of thefunction is required). Distributed execution of code, such as agents, onthe nodes of the cluster allows the distributed data grid to operate asa distributed computing environment.

In an Oracle® Coherence data grid, the distributed cache service (e.g.,132 a, 132 b, 132 c, 132 d, 132 e) is the service which provides fordata storage in the distributed data grid and is operative on all nodesof the cluster that read/write/store cache data, even if the node isstorage disabled. The distributed cache service allows cluster nodes todistribute (partition) data across the cluster 100 a so that each pieceof data in the cache is managed primarily (held) by only one clusternode. The distributed cache service handles storage operation requestssuch as put, get, etc. The distributed cache service manages distributedcaches (e.g., 140 a, 140 b, 140 c, 140 d, 140 e) defined in adistributed schema definition and partitioned among the nodes of acluster.

A partition is the basic unit of managed data in the distributed datagrid and stored in the distributed caches (e.g., 140 a, 140 b, 140 c,140 d, and 140 e). The data is logically divided into primary partitions(e.g., 142 a, 142 b, 142 c, 142 d, and 142 e), that are distributedacross multiple cluster nodes such that exactly one node in the clusteris responsible for each piece of data in the cache. Each cache (e.g.,140 a, 140 b, 140 c, 140 d, and 140 e) can hold a number of partitions.Each partition (e.g., 142 a, 142 b, 142 c, 142 d, 142 e) may hold onedatum or it may hold many. A partition can be migrated from the cache ofone node to the cache of another node when necessary or desirable. Forexample, when nodes are added to the cluster, the partitions aremigrated so that they are distributed among the available nodesincluding newly added nodes. In a non-replicated distributed data gridthere is only one active copy of each partition (the primary partition).However, there is typically also one or more replica/backup copy of eachpartition (stored on a different server) which is used for failover.Because the data is spread out in partition distributed among theservers of the cluster, the responsibility for managing and providingaccess to the data is automatically load-balanced across the cluster.

The distributed cache service can be configured so that each piece ofdata is backed up by one or more other cluster nodes to support failoverwithout any data loss. For example, as shown in FIG. 1, each partitionis stored in a primary partition (e.g., dark shaded squares 142 a, 142b, 142 c, 142 d, and 142 e) and one or more synchronized backup copy ofthe partition (e.g., light shaded squares 144 a, 144 b, 144 c, 144 d,and 144 e). The backup copy of each partition is stored on a separateserver/node than the primary partition with which it is synchronized.Failover of a distributed cache service on a node involves promoting thebackup copy of the partition to be the primary partition. When aserver/node fails, all remaining cluster nodes determine what backuppartitions they hold for primary partitions on failed node. The clusternodes then promote the backup partitions to primary partitions onwhatever cluster node they are held (new backup partitions are thencreated).

A distributed cache is a collection of data objects. Each dataobject/datum can be, for example, the equivalent of a row of a databasetable. Each datum is associated with a unique key which identifies thedatum. Each partition (e.g., 142 a, 142 b, 142 c, 142 d, 142 e) may holdone datum or it may hold many and the partitions are distributed amongall the nodes of the cluster. In an Oracle® Coherence data grid each keyand each datum is stored as a data object serialized in an efficientuncompressed binary encoding called Portable Object Format (POF).

In order to find a particular datum, each node has a map, for example ahash map, which maps keys to partitions. The map is known to all nodesin the cluster and is synchronized and updated across all nodes of thecluster. Each partition has a backing map which maps each key associatedwith the partition to the corresponding datum stored in the partition.An operation associated with a particular key/datum can be received froma client at any node in the distributed data grid. When the nodereceives the operation, the node can provide direct access to thevalue/object associated with the key, if the key is associated with aprimary partition on the receiving node. If the key is not associatedwith a primary partition on the receiving node, the node can direct theoperation directly to the node holding the primary partition associatedwith the key (in one hop). Thus, using the hash map and the partitionmaps, each node can provide direct or one-hop access to every datumcorresponding to every key in the distributed cache.

In some applications, data in the distributed cache is initiallypopulated from a database 110 comprising data 112. The data 112 indatabase 110 is serialized, partitioned and distributed among the nodesof the distributed data grid. Distributed data grid 100 stores dataobjects created from data 112 from database 110 in partitions in thememory of servers 120 a, 120 b, 120 c, 120 d such that clients 150and/or applications in data grid 100 can access those data objectsdirectly from memory. Reading from and writing to the data objects inthe distributed data grid 100 is much faster and allows moresimultaneous connections than could be achieved using the database 110directly. In-memory replication of data and guaranteed data consistencymake the distributed data grid suitable for managing transactions inmemory until they are persisted to an external data source such asdatabase 110 for archiving and reporting. If changes are made to thedata objects in memory the changes are synchronized between primary andbackup partitions and may subsequently be written back to database 110using asynchronous writes (write behind) to avoid bottlenecks.

Although the data is spread out across cluster nodes, a client 150 canconnect to any cluster node and retrieve any datum. This is calledlocation transparency, which means that the developer does not have tocode based on the topology of the cache. In some embodiments, a clientmight connect to a particular service e.g., a proxy service on aparticular node. In other embodiments, a connection pool or loadbalancer may be used to direct a client to a particular node and ensurethat client connections are distributed over some or all the data nodes.However connected, a receiving node in the distributed data gridreceives tasks from a client 150, and each task is associated with aparticular datum, and must therefore be handled by a particular node.Whichever node receives a task (e.g. a call directed to the cacheservice) for a particular datum identifies the partition in which thedatum is stored and the node responsible for that partition, thereceiving node, then directs the task to the node holding the requestedpartition for example by making a remote cache call. Since each piece ofdata is managed by only one cluster node, an access over the network isonly a “single hop” operation. This type of access is extremelyscalable, since it can use point-to-point communication and thus takeoptimal advantage of a switched fabric network such as InfiniBand.

Similarly, a cache update operation can use the same single-hoppoint-to-point approach with the data being sent both to the node withthe primary partition and the node with the backup copy of thepartition. Modifications to the cache are not considered complete untilall backups have acknowledged receipt, which guarantees that dataconsistency is maintained, and that no data is lost if a cluster nodewere to unexpectedly fail during a write operation. The distributedcache service also allows certain cluster nodes to be configured tostore data, and others to be configured to not store data.

In some embodiments, a distributed data grid is optionally configuredwith an elastic data feature which makes use of solid state devices(e.g. SSD 128 a), most typically flash drives, to provide spillovercapacity for a cache. Using the elastic data feature a cache isspecified to use a backing map based on a RAM or DISK journal. Journalsprovide a mechanism for storing object state changes. Each datum/valueis recorded with reference to a specific key and in-memory trees areused to store a pointer to the datum (a tiny datum/value may be storeddirectly in the tree). This allows some values (data) to be stored insolid state devices (e.g. SSD 128 a) while having the index/memory treestored in memory (e.g. RAM 124 a). The elastic data feature allows thedistributed data grid to support larger amounts of data per node withlittle loss in performance compared to completely RAM-based solutions.

A distributed data grid such as the Oracle® Coherence data griddescribed above can improve system performance by solving data operationlatency problems and by caching and processing data in real time.Applications cache data in the data grid, avoiding expensive requests toback-end data sources. The shared data cache provides a single,consistent view of cached data. Reading from the cache is faster thanquerying back-end data sources and scales naturally with the applicationtier. In memory performance alleviates bottlenecks and reduces datacontention, improving application responsiveness. Parallel query andcomputation is supported to improve performance for data-basedcalculations. The distributed data grid is fault-tolerant, providing fordata reliability, accuracy, consistency, high availability, and disasterrecovery. The distributed data grid enables applications to scalelinearly and dynamically for predictable cost and improved resourceutilization. For many applications, a distributed data grid offers avaluable shared data source solution.

In embodiments of the present invention, the distributed data grid 100of FIG. 1 implements one or more system and method for persistence ofthe data within the caches 140 a, 140 b, 140 c, 140 d, and 140 e.Persistence features of the distributed data grid manage the persistenceand recovery of distributed caches. Cached data is persisted so that itcan be quickly recovered after a catastrophic failure or after a clusterrestart due to planned maintenance. Enabling on-demand persistence, acache service is manually persisted and recovered upon request using thesnapshot coordinator 160. The snapshot coordinator 160 providesoperations for creating, archiving, and recovering snapshots of a cacheservice. A persistence policy may also be used to schedule persistenceat predetermined intervals, times or upon the occurrence of definedevents.

Persistence uses a persistence store to store copies of the backing mapof partitions of a partitioned service. The persistence files can bestored on the local disk (e.g. SSD 128 a of each server or on a shareddisk on a storage area network (SAN) 162 or in a database 110. The localdisk option allows each cluster member to access persisted data for theservice partitions that it owns. Local disk storage provides a highthroughput and low latency storage mechanism. The shared disk optionallows each cluster member to access persisted data for all servicepartitions. Both the local disk and shared disk approach can rely on aquorum policy that controls how many cluster members must be present toperform persistence operations and before recovery can begin. Quorumpolicies allow time for a cluster to start before data recovery begins.

Each cache service (e.g. 132 a, 132 b, 132 c, 132 d, and 132 e) isoperated on a single cache service thread operating on a cluster member(the service thread may utilize a pool of worker threads). In priorpersistence mechanisms, operation of the cache service threads wassuspended during creation of a snapshot of all the partitions served bythe cache service thread. Consequently, all partitions under control ofthe cache service thread were unavailable for read or write requestsduring the persistence of the entire contents of the cache served by thecache service thread. The cache service thread was only reactivatedafter completion of the snapshot. Suspending the cache service thread isdisadvantageous because it reduces the availability of data in thecaches.

Partition-Scoped Snapshot Creation

In some situations, it may be necessary or desirable to make apersistent snapshot of a data grid cluster. A distributed data grid cansupport various cache services using an in-memory data store. The systemallows a user to use a management tool to take a snapshot of thein-memory data store that supports the cache services on-demand, at anyparticular time. In prior systems cache services were suspended acrossthe cluster, prior to taking the snapshot of the cluster. Thus, thesystem provided a globally consistent point in time for taking thesnapshot, i.e. all partitions were copied at the same point in time withno deviations because all read and write operations were suspendedduring creation of the snapshot. Then, the cache service was resumedafter the snapshot was completed. The snapshot provided a globallyconsistent view of the entire cache service. For example, the snapshotprovided a catalogue of all state information of the system at aparticular point in time, including metadata and cache data for thecache services. Additionally, the system could store/persist thesnapshot either in a central location such as a storage area network orin distributed local disks. See, e.g. U.S. patent application Ser. No.14/271,161 titled “SYSTEM AND METHOD FOR PROVIDING A PERSISTENT SNAPSHOTOF A RUNNING SYSTEM IN A DISTRIBUTED DATA GRID” filed May 6, 2014 whichis incorporated herein by reference.

However, because snapshots are made, maintained and restored on apartition by partition basis, it is not essential to make all of thesnapshots of all of the individual partitions at the same point in time.To achieve scalable transactions in a distributed data architecture,affinity or pinning processes ensure that any related data is stored inthe same partition thus can be atomically updated. To put it anotherway, data in different partitions is generally independent of data inother partitions. Accordingly, it is unnecessary to have all snapshotsof partitions made at the same point in time to provide a globallyconsistent view of the whole cluster. Inter-partition synchronization isnot important and the need to quiesce/suspend all cache services inorder to achieve inter-partition synchronization has substantial costsin terms of service unavailability.

In embodiments of the present invention, snapshots are created undercontrol of the snapshot coordinator 160 with partition-scoped atomicity.That is, snapshots are created a partition-at-a time rather than all atthe same point in time. This feature allows cache service threads toremain running while a persistent snapshot of the distributed data gridis created. Accordingly, the cache service threads need not be suspendedduring the partition-scoped snapshot process. Rather than deactivatingcache service threads for all partitions during creation of a snapshot,the partition-scoped snapshot process effectively blocks processing ofpersistent tasks on a particular partition during creation of a snapshot(copying of the backing map to the designated store) on that particularpartition and then releases the block after the snapshot has beencreated. The partitioned snapshot process iterates over all thepartitions in each node of the cluster in order to obtain snapshots ofall the partitions.

This partition-by-partition process allows all partitions which are notin the process of being copied/persisted to continue responding to readand write requests via the still running cache service. Moreover foreach particular partition, execution of write requests is only blockedduring the period that a partition-scoped snapshot creation task isbeing performed for the particular partition and execution of read-onlyrequests can continue even during snapshot creation. Consequently use ofthe partition-scoped snapshot process described herein increases theavailability of data in the cache service compared to the prior snapshotsystems and methods. Accordingly, the partitioned snapshot creationsystem and method improves the performance of a distributed data grid byavoiding the need to suspend cache service threads during snapshots andincreasing the availability of data to users of the system via clients150.

Referring again to FIG. 1, when partition-scoped snapshot creation istriggered, Snapshot Coordinator 160 sends snapshot requests to each nodein the cluster. The snapshot requests have partition-scoped atomicity.To put it another way an individual request is sent from the snapshotcoordinator to each node identifying each primary partition on the nodefor which a snapshot is desired. Each request specifies the task orpersisting the identified primary partitions for which it is issued andincludes a timeout period. If particular partition-scoped snapshots arenot completed within the timeout period the request fails and an errormessage is returned to the snapshot coordinator. The snapshotcoordinator 160 retries with a new partition-scoped snapshot request forany partitions where the original request failed until allpartition-scoped snapshots have been prepared for every partition in thecluster (or the desired subset of all partitions).

The snapshot process staggers partition-scoped snapshot creation suchthat it is performed iteratively—partition-by-partition for each clustermember in the service. Staggering of the partition-scoped snapshotrequests can be used to control the amount of resources being used forsnapshot creation at any point in time. For example, the snapshotcoordinator will send a snapshot request to each member requesting thecreation of a snapshot for all of the partitions it owns. The nodereceiving the request will iterate over each partition creating asnapshot of each partition it owns, and respond to the coordinator withthe identity of partitions that succeeded and the elapsed times tocreate the snapshots. The coordinator will subsequently request for anyfailed partitions to be snapshot and maintain statistics for the elapsedtimes. These statistics are used in future partition-scoped snapshotrequests to reduce the number of members contacted in an iteration thusreduce the observability of the request. Ultimately the algorithmspurpose is to determine an ideal compromise between availability andtotal request time.

When a partition-scoped snapshot request is received at a clustermember/node, the cluster member performs partition-scoped snapshot taskssequentially for all of the partitions identified in the request. Beforeeach partition-scoped snapshot task, the cluster member/node must firstprocess all pending (received before the partition-scoped snapshotrequest) transaction requests directed at the identified partition.After draining the pending transaction requests, the cluster memberprocesses the partition-scoped snapshot request by persisting a copy ofthe identified partition to the designated persistence store (either thelocal disk or shared disk). Upon completion of each partition-scopedsnapshot task, the cluster member either proceeds to the next identifiedpartition, or sends a response to the cluster coordinator (if nopartitions remain). If the snapshot is not created the cluster membersends a response to the cluster coordinator indicating fail. Theresponse includes the successful partitions, failed partitions andlatencies. If a particular snapshot of a partition times out orotherwise fails, the snapshot coordinator sends another partition-scopedsnapshot request for the partition at a later time.

During the making of the copy of the partition, the cache service threadis not suspended. Additional transaction requests directed at theidentified partition may be received during the copying process. Inorder to ensure intra-partition consistency, no persistent transactionrequests directed at the identified partition (e.gg. write operations)are allowed to proceed during creation of the snapshot of the partition.Processing of persistent tasks directed to the identified partition istherefore deferred until after completion of the snapshot for theidentified partition. However, processing of persistent transactions onthe identified partition is resumed as soon as the snapshot iscompleted. Moreover, transactions directed at other partitions served bythe cache service thread and non-persistent transactions directed at thepartition being snapshot (can continue to be processed. Thus, theremainder of cache remains available for transactions during thesnapshot of the identified partition. The duration of unavailability ofeach partition is therefore substantially reduced as compared to systemswhich suspend the cache service threads through a cluster during theentire snapshot process for all partitions. In general terms, readavailability of cache data is not interrupted and write availability isonly interrupted during executions of the snapshot task for theparticular partition in which the data is stored.

In some embodiments, the partition-scoped snapshots are persistedinitially to the local disk on the node holding the partition.Subsequently an archiver 170 (see FIG. 1) copies the partition-scopedsnapshots from the local disks on the nodes to a central location e.g.Storage area network 172. The partition-scoped snapshots may then becompiled into a single archive unit and the single archive unit 174persisted at the central location. An archiver suitable for use inconjunction with the partition-scoped snapshot creation is described inU.S. patent application Ser. No. 15/200,887 entitled “SYSTEM AND METHODFOR DISTRIBUTED PERSISTENT STORE ARCHIVAL AND RETRIEVAL IN A DISTRIBUTEDCOMPUTING ENVIRONMENT” filed on Jul. 1, 2016.

FIG. 2 illustrates a method for partition-scoped snapshot creation in adistributed data grid, in accordance with an embodiment of theinvention. The left column shows actions of Snapshot Coordinator 200.The right column shows the actions of Nodes 250 with respect to eachpartition-scoped snapshot request. As shown in FIG. 2, at step 202, thesnapshot coordinator initiates partition-scoped snapshot creation atstep 202. The initiation can be based on user request or a policy or aschedule.

At step 204, the snapshot coordinator iterates snapshot requests overall the members/nodes for which partition-scoped snapshots are required.Typically the snapshot process will include all partitions in a cluster.In an alternative embodiment, for example in a multi-tenant distributeddata grid, a subset of including less than all partitions in the clusterwill be selected. For example the partition-scoped snapshot creation maybe limited to only those partitions owned by a particular tenant.Whether some or all of the partitions are selected, the snapshotcoordinator sends a different snapshot request to each member/nodeidentifying the partitions owned by the node for which partition-scopedsnapshots are to be created.

Multiple snapshot requests can be in process on different servers/nodessimultaneously. However it is preferable that snapshot creation does notconsume all available threads/processing power on the servers/nodesbecause that would impair performance of other services. Thus,initiation of the partition-scoped snapshot creation may be staggered tocontrol the amount of resources being used for snapshot creation at anypoint in time. For example, snapshot coordinator may send only one ortwo snapshot requests at a time. In such case, new snapshot requests(for other server/node) are only transmitted after a response isreturned from the particular server node (success or fail).Alternatively, the snapshot coordinator may transmit snapshot requeststo nodes at particular time intervals selected to permit completion ofthe prior partition-scoped snapshot request.

Referring again to FIG. 2, steps 210-216 show the process performed bythe snapshot coordinator for each partition-scoped snapshot. Thisprocess is repeated for each of the cluster members. At step 210, thesnapshot coordinator sends a snapshot request identifying particularpartitions to the server node holding the particular partitions. Therequest includes a snapshot name and a set of partitions for whichsnapshots art to be made. Each snapshot process is created with a uniquename which is exposed via the snapshot coordinator. This unique nameallows future operations on the collection of partition-scoped snapshotscreated in the same snapshot process to occur including recover fromsnapshot and archive/retrieve snapshot. The individual partition-scopedsnapshots can be identified for recovery by the unique snapshot name andthe partition identifier/key.

At step 212, the snapshot coordinator receives a partition-scopedsnapshot response from the server node holding the particular partition.The response includes the successful partitions, failed partitions andlatencies. The response indicates success or failure for the task ofpersisting a copy of the particular partitions identified in the requestto the designated persistence store. At step 214 if the partition-scopedsnapshot creation was not successful with respect to one or morepartitions, the snapshot coordinator retries the process starting atstep 210, such retry may be immediate or after a period of time. Thesnapshot coordinator can use the latency information from the snapshotresponse to inform when to retry a snapshot or to adjust future snapshotprocesses. At step 214, the snapshot coordinator can retry snapshots offailed partitions by sending new snapshot requests directed at thefailed partitions. At step 216 if the partition-scoped snapshot creationwas successful or a partition failed the snapshot by exceeding athreshold, the snapshot coordinator records the successful and failedpartitions and emits a JMX Notification at step 218 to ensure anysubscribers are aware of the result of the request.

Referring again to FIG. 2, steps 252-258 show the process performed bythe nodes for each snapshot request and partition-scoped snapshot task.This process is repeated by each of the nodes for each of the partitionsheld by the nodes. At step 252, the node receives a partition-scopedsnapshot request identifying particular partitions from the snapshotcoordinator. A timeout period is applied to the request such that therequest fails if not performed within the timeout period. The requestincludes a snapshot name and a set of partitions for which snapshots artto be made. For each identified partition the node performs the task ofpersisting a copy of the particular partition to the designatedpersistence store.

The request is processed on the cache service thread and then individualpartition-scoped snapshot tasks are dispatched sequentially forexecution (e.g. to an association pile as described below). A timeoutperiod is applied to each partition-scoped snapshot task such that thetask fails if not performed within the timeout period. The taskcomprises persisting a copy of the particular partition to thedesignated persistence store. Thus the node iterates the snapshot tasksover all partitions identified in the snapshot request.

Prior to performing a snapshot task for a particular partition, at step254, the node drains any pending persistent tasks targeting theidentified partition. Then at step 256, the node performs the task ofpersisting a copy of the particular partition to the designatedpersistence store. During the persisting step, the node does not suspendthe cache service and continues to process requests against partitionsnot in the persisting step. However during the persisting step, the nodeblocks persistent tasks targeting the particular partition undergoingthe persisting step. This is to ensure that no changes are made to theparticular partition during the persisting step. The blocking ofpersistent tasks (e.g. write operations) received after thepartition-scoped snapshot request is underway can be achieved in anumber of ways. One way is for the persisting process to obtain a lockon the partition to prevent other requests from accessing the partition.Another way is through use of the association pile described below.Blocking, as used herein, means preventing the processing of otherpersistent tasks against the particular partition, however it isachieved, whether through locking the partition, utilization of anassociation pile, or by other means. After completion of each snapshottask the node may then resume processing persistent tasks targeting theparticular partition, including any persistent tasks which accumulatedduring the snapshot persisting step.

When a particular individual partition-scoped snapshot task has beenexecuted it finalizes the task on the service thread which proceeds atstep 257 to snapshot the next partition or respond to the snapshotcoordinator if no partitions remain. Accordingly on a particular nodethe snapshot tasks are staggered (performed sequentially) to preventsaturating all of the available worker threads with the snapshot processand impairing performance of other tasks by the cache service. Uponcompletion of the task of persisting copies of all the partitionsidentified in the snapshot request to the designated persistence store(or timeout or failure), at step 258 the node sends a snapshot responseto the snapshot coordinator. The response includes the successfulpartitions, failed partitions and latencies. The response indicatessuccess or failure for the task of persisting a copy of the particularpartitions identified in the request to the designated persistence storeand durations of the snapshot tasks.

At step 216 if the partition-scoped snapshot creation was successful ora partition failed the snapshot by exceeding a threshold, the snapshotcoordinator records the successful and failed partitions and emits a JMXNotification at step 218 to ensure any subscribers are aware of theresult of the request. As described above, upon completion of thepartition-scoped snapshot creation for each of the partitions, anoptional archiver can perform an archive process to copy all thepartition-scoped snapshot to a central location for compilation into asingle archive unit.

Partitioned Snapshot Creation Utilizing An Association Pile

In an embodiment, the partitioned snapshot system and method isimplemented for cache service threads utilizing an association pilehaving a scalable thread pool of worker threads. The scalable threadpool provides the cache service thread with additional processingresources and a system. In particular, the scalable thread pool exhibitshigh performance on multi-core systems and is suitable for providing acache service thread of a distributed data grid with additional workerthreads when required thereby improving performance of the distributeddata grid. A data structure for providing work to worker threadscomprises work slots and association piles which reduce and/or eliminatecontention while allowing scaling of the thread pool. The scalablethread pool as described has particular utility to cache service threadsin the distributed data grid described below with respect to FIG. 1.Additional features of the scalable thread pool are described in U.S.patent application Ser. No. 14/857,458 entitled SYSTEM AND METHOD FORSUPPORTING A SCALABLE THREAD POOL IN A DISTRIBUTED DATA GRID FILED Sep.17, 2015 which is hereby incorporated by reference.

The scalable thread pool can be used to implement the partition-scopedsnapshot creation on the server nodes. The snapshot coordinator can sendrequests with partition-scoped atomicity to nodes requesting they makesnapshots (copies) of identified partitions. The cache service whichreceives the requests then iterates a snapshot process over theidentified partitions generating partition-scoped snapshot tasks foreach identified partition. The partitions-scoped snapshot tasks aredispatched to the scalable thread pool for execution. All tasks thatinvolve writing to the persistent store for a particular partition(persistent tasks) are effectively single-threaded through anassociation pile. The snapshot task for a particular partition is addedto the back of an association pile through which all persistent taskstargeting the partition are directed.

For the purposed of this embodiment a cache service write request can bebroken down into two sections; processing the primary storage,persisting to the recoverable store and responding to the sender of therequest. The persisting to the recoverable store is a separate taskadded to the association pile with the same association as any otherpersistent tasks for a particular partition, including the snapshottask. The write request only responds to the sender upon completion ofthe ‘persisting to recoverable store’ section of the request. Thissegmented hand-off of associated work introduces some novel traits; forthis scenario it allows the snapshot task to execute with exclusiveaccess to the persistent store but allows read and write cache servicerequests to complete. Read requests will execute without being impeded.However write requests that are processed for the same partition afterthe snapshot task was added to the pile will wait for completion of thesnapshot task before the write request task can complete and the senderof the request be responded to.

When all prior received persistent tasks directed to the particularpartition have been processed, the partition-scoped snapshot createdtask is polled from the association pile to begin processing of thesnapshot persisting task. The association pile only allows onepersistent task related to a particular partition to be polled at atime. Thus, the snapshot task runs on the only worker thread processingpersistent tasks related to the partition at the time (therebypreventing any other persistent tasks operating on partition untilcomplete). This blocks processing of any other persistent tasks directedto the particular partition without the need for an explicit lock on thepartition. Persistent tasks directed at the partition can still be addedto association pile during execution of the snapshot task. However theywon't be polled from the association pile until the snapshot task iscompleted and the worker thread is released. Non persistent tasks (e.g.read requests) can still be performed by the cache service. Thus thereis no need to suspend the cache service thread during snapshotting. Whenthe worker thread finishes snapshot task, it is released and it can moveon to other tasks queued in the association pile. Thus, cache serviceavailability is improved relative to prior systems which requiredquiescing the cache service during snapshot creation.

FIG. 3 shows an overview of a scalable thread pool system suitable foruse in a distributed data grid. The scalable thread pool system can beconfigured to implement functionality of the partition-scoped snapshotsystem and method. As shown in FIG. 3, a cache service thread 132 aoperating on a node 130 a in the cluster 100 a of the distributed datagrid can receive requests from any of the other nodes in the cluster(e.g. nodes 130 b, 130 c, 130 d, and 130 e). The requests are directedfor execution against particular partitions in the cache 140 a. Inembodiments of the present invention the cache service thread 132 a canalso receive snapshot requests from the snapshot coordinator 160 e.g.Snapshot request 330. Each snapshot request provides for copying thebacking map associated with one or more identified partitions to thedesignated persistence store.

The cache service thread 132 a processes messages, including snapshotrequest 330 in the order in which they are received by the cache servicethread 132 a. In response to the messages the cache service generatestasks (e.g. tasks 311-315) which are added to the association pile forexecution. Additionally, in response to the snapshot request 330, thecache service may generate a snapshot task 331 for creating a snapshotof partition 142 b. The scalable thread pool 302 contains one ortypically more worker threads 321-323. The association pile 310 holdsone or more elements (e.g. the tasks 311-315 and snapshot task 331).Furthermore, the system allows multiple threads (e.g. the worker threads321-323) to poll elements from the association pile 310 in parallel.Additionally, the system prevents an element, which is held in theassociation pile 310 and has an association with a previously polledelement, from being polled until the previously polled associatedelement has been released. Persistent tasks directed to the samepartition have the same association. Thus, the system can prevent apersistent task in the association pile keyed to a particular partitionfrom being processed while another persistent task keyed to the samepartition is being executed by a worker thread.

An association pile, such as association pile 310, is a data structurethat holds elements in a loosely ordered way with a queue-like contract.The association pile respects the possibility that some elements can beassociated with one another by way of an associated key. Elementsassociated with the same key should maintain first-in-first-out (FIFO)ordering, but may be re-ordered with respect to elements associated withdifferent keys. The key may be, for example, the unique key whichidentifies a partition (e.g. partition 142 a) in the distributed datagrid as described above. Only one thread can operate on persistent tasksfor a particular partition at a time and persistent operations performedon a particular partition should be performed in the order they arereceived. This is because all persistent tasks for a partition have thesame association and thus can only be polled sequentially uponcompletion of the preceding task. Accordingly an association pile can,for an example, maintain first-in-first-out (FIFO) ordering ofpersistent operations performed on a particular partition associatedwith a same unique key.

Elements can be added to and removed from an association pile. Elementsare added to the association pile by a calling thread (e.g. cacheservice thread 132 a). Elements are removed from an association pile bya worker thread (e.g. 321, 322, and 323). Removing an element isperformed in two steps: first an available element is removed by aworker thread “polling” the association pile; second when the workerthread is finished with the element it is “released” from theassociation pile. The association pile 310 assumes that polled-butnot-yet-released elements are being processed on a worker thread andtherefore prevents polling of any element associated with the same keyas a polled-but not-yet-released element. Thus, the system can prevent apersistent task in the association pile keyed to a particular partitionfrom being processed while another persistent task keyed to the samepartition is pending.

Accordingly, using the association pile, elements in the associationpile directed to the same partition in cache 140 a are processed in theorder they are received. When Snapshot Request Task 331 for partition142 a is placed in the association pile 310, it will be maintained infirst-in-first-out (FIFO) ordering with respect to other persistenttasks directed at partition 142 a. Moreover the association pile ensuresthat only one persistent task directed at partition 142 a is polled andexecuted by a worker thread at time. Accordingly, if there are pendingpersistent tasks directed to partition 142 a they will be processed andcompleted in order of receipt until the Snapshot Task 3331 is passed tothe worker thread. Accordingly the association pile ensures that allpersistent tasks directed to partition 142 a are drained/completedbefore commencing the persisting task specified by the snapshot task331. If, for example tasks 311, 312, 314 are persistent tasks directedto partition 142 a they will be executed and completed before snapshottask 331.

The worker thread will then perform the persisting task specified by thesnapshot task 331 by copying the backing map of the partition 142 a tothe designated persistence store for creating the snapshot of partition142 a. During the data transfer, additional persistent tasks directed atpartition 142 a (and thus having the same association key) willaccumulate in the association pile 310. If, for example messages 314,315 are persistent tasks directed to partition 142 a they willaccumulate in the association pile 310 but not be completed prior to therelease of the snapshot persisting task 331.

Because processing is essentially single-threaded (only one persistenttask directed at a partition can be polled at a time) with respect topartition 142 a, no completion of the other persistent tasks forpartition 142 a will occur during the partition-scoped snapshot process.This effectively blocks write access to partition 142 a ensuring thesnapshot of the partition 142 a is consistent (no data changed duringthe snapshot) without necessitating placing a lock on the partition 142a (using e.g. compare-and-set CAS). Moreover cache service processing ofpersistent tasks with respect to the particular partition 142 a iseffectively blocked (write requests are added to the association pile)without requiring suspending operation of the cache service thread 132 aas to the particular partition (or any other partition). Thus, if forexample messages 314, 315 are directed to different partition 342 a theycan be placed by cache service thread 132 a into the association pile310 and may be processed on a different worker thread even duringperformance of the persisting task with respect to partition 142 a.Moreover non-persistent (e.g. read only) tasks directed to partition 142a can be processed in parallel on other threads even during the snapshottask for partition 142 a.

FIGS. 4A and 4B show a more detailed example of a cache service thread401 utilizing association piles and a scalable thread pool. As shown inFIGS. 4A and 4B, a cache service thread 401 is associated with ascalable thread pool 400. Thread pool 400 has of a fixed number (CPUcount) of work slots 410. Four work slots 410 a, 410 b, 410 c, and 410 dare shown. Each work slot has a thread gate which can either be open orclosed. When the thread gate is open a thread can enter and exit thegate. When a thread has entered a gate, the gate cannot be closed. Whenthe thread gate is closed threads cannot enter the gate.

When work is added to the thread pool by the service thread, the work isdistributed across the work slots. The service thread adds the work tothe slot 410 a, 410 b, 410 c, or 410 d with the smallest backlog (i.e.the slot with the smallest association pile) with some randomness.However all work associated with the same key is added to the same slotin order to preserve ordering of associated work. Accordingly all workassociated with a same partition goes to the same one of the work slots.When work is added to a work slot of a thread pool, the calling threadenters the thread gate of the work slot and adds the work to anassociation pile as described below.

A thread pool's fixed number (CPU count) of work slots are linked to oneor more worker threads by way of one or more association pile. Thethread pool has a dynamic number of association piles 420. Each workslot is associated with exactly one association pile. However multiplework slots may share the same association pile. When work is added to awork slot of the thread pool, the calling thread enters the thread gateof the work slot and adds the work to one of the association piles. Allwork added through a particular work slot is directed to the particularassociation pile associated with that work slot. All work related to aparticular key (e.g. a particular partition) is added through the samework slot and, thus, is directed to the same association pile. Thus allwork related to a particular partition is directed to the sameassociation pile.

A thread pool also has a dynamic number of worker threads 430. Eachworker thread is associated with exactly one association pile. Theworker threads poll work form the association piles. But, each workerthread only polls work from the one association pile with which theworker thread is associated. Multiple worker threads can be associatedwith the same association pile and poll work from it. As shown in FIGS.4A and 4B, the number of worker threads and the number of associationpiles in the scalable thread pool can change over time as worker threadsare added or removed according to the methods described with respect toFIGS. 4A, 4B, 5, 6.

FIG. 4A shows a configuration where the number of worker threads 430 isgreater than the number of work slots 412. When there are more workerthreads than work slots, the number of association piles equals thenumber of work slots. In the configuration of FIG. 4A there are sevenactive worker threads 430 a, 430 b, 430 c, 430 d, 430 e, 430 f, and 430g. Thus, as shown in FIG. 4A, because there are four work slots 410 a,410 b, 410 c, and 410 d there are also four association piles 420 a, 420b, 420 c, and 420 d. As shown in FIG. 4A, each work slot has a dedicatedassociation pile into which a calling thread which enters the slotplaces work. All work related to a particular key (e.g. a sameassociation) is added through the same work slot and, thus, is directedto the same dedicated association pile. There are more worker threadsthan piles, therefore at least some of the worker threads 430 a, 430 b,430 c, 430 d, 430 e, 430 f, and 430 g need to share some of theassociation piles. That is to say more than one worker thread can beremoving work from each association pile.

For example, worker threads 430 a and 430 b both poll work fromassociation pile 420 a. Elements are removed from an association pile bya worker thread. Removing an element is performed in two steps: first anavailable element is removed by a worker thread “polling” theassociation pile; second when the worker thread is finished with theelement it is “released” from the association pile. The association pileassumes that polled-but not-yet-released elements are being processedand therefore prevents polling of any element associated with the samekey as a polled-but not-yet-released element. This ensures, for example,that only a single worker thread is utilized at a time for processingpersistent tasks with respect to a particular partition associated witha particular key and thus ensures only a single persistent task at atime can be processed with respect to a partition at a time. Thusalthough two worker threads are available for performing work in theassociation pile only one thread can be performing persistent tasks fora particular partition at a time. This results essentially insingle-threaded execution with respect to each partition with respect topersistent tasks.

Because the system is configured such that only a single persistent taskat a time can be processed with respect to a partition, if that task isa partition-scoped snapshot, the effect is to block access to thepartition to write requests directed to the partition during thesnapshot persisting process. Any persistent tasks (e.g. write requests)received by cache service thread 401 and directed to the same partitionare added to the work slot and association pile used by for workdirected at the partition, but are not processed until the worker threadreturns on completion (release) of the snapshot persisting task.Notably, the cache service thread continues to operate (is notsuspended) and tasks directed at other partitions can still be processedthrough the work slots, association piles and worker threads other thanthe worker thread performing the persisting task. Moreovernon-persistent tasks (e.g. read requests) can be processed on otherthread in parallel even as to the partition being snapshot.

FIG. 4B shows a configuration where the number of worker threads 430 isless than the number of work slots 412. Where there are less workerthreads than work slots, the number of association piles equals thenumber of work threads. Thus, FIG. 4B shows three worker threads 430 a,430 b, 430 c and three association piles 420 a, 420 b, and 420 c. Thereare more work slots 410 than association piles 420, so some work slotsmust share piles. For example, the calling threads that enter work slots410 c and 410 d may both place work in association pile 420 c. However,all work related to a particular key is added through the same work slotand, thus, is still directed to the same (shared) association pile. Asshown in FIG. 4B, each worker thread however has a dedicated associationpile from which it removes work. However, if a worker thread has nothingto do, it attempts to pull work from another thread's pile before goingto sleep. The association pile will still prevent polling of workdirected to the same partition as works which has been polled but notyet released.

While various embodiments of the present invention have been describedabove, it should be understood that they have been presented by way ofexample, and not limitation. It will be apparent to persons skilled inthe relevant art that various changes in form and detail can be madetherein without departing from the spirit and scope of the invention.

Many features of the present invention can be performed in, using, orwith the assistance of hardware, software, firmware, or combinationsthereof. The present invention may be conveniently implemented using oneor more conventional general purpose or specialized digital computer,computing device, machine, or microprocessor, including one or moreprocessors, memory and/or computer readable storage media programmedaccording to the teachings of the present disclosure. Features of theinvention may also be implemented in hardware using, for example,hardware components such as application specific integrated circuits(ASICs) and programmable logic device. Implementation of the hardwarestate machine so as to perform the functions described herein will beapparent to persons skilled in the relevant art.

Features of the present invention can be incorporated in software and/orfirmware for controlling the hardware of a processing system, and forenabling a processing system to interact with other mechanisms utilizingthe results of the present invention. Such software or firmware mayinclude, but is not limited to, application code, device drivers,operating systems and execution environments/containers. Appropriatesoftware coding can readily be prepared by skilled programmers based onthe teachings of the present disclosure, as will be apparent to thoseskilled in the software art.

In some embodiments, the present invention includes a computer programproduct which is a storage medium or computer readable medium (media)having instructions stored thereon/in which can be used to program acomputer to perform any of the processes of the present invention. Thestorage medium or computer readable medium can include, but is notlimited to, any type of disk including floppy disks, optical discs, DVD,CD-ROMs, microdrive, and magneto-optical disks, ROMs, RAMs, EPROMs,EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic or optical cards,nanosystems (including molecular memory ICs), or any type of media ordevice suitable for storing instructions and/or data. In embodiments,the storage medium or computer readable medium can be non-transitory.

The foregoing description of the present invention has been provided forthe purposes of illustration and description. It is not intended to beexhaustive or to limit the invention to the precise forms disclosed.Many modifications and variations will be apparent to the practitionerskilled in the art. The embodiments were chosen and described in orderto best explain the principles of the invention and its practicalapplication, thereby enabling others skilled in the art to understandthe invention for various embodiments and with various modificationsthat are suited to the particular use contemplated. It is intended thatthe scope of the invention be defined by the following claims and theirequivalents.

What is claimed is:
 1. A method for supporting partition-scoped snapshotcreation in a distributed data grid comprising a plurality of nodes, themethod comprising: sending a snapshot request from a snapshotcoordinator to a node storing a plurality of partitions, wherein thesnapshot request includes a snapshot name and a list of partitions;receiving the snapshot request at the node; and persisting a copy ofeach partition identified in the list of partitions to a persistencestore without quiescing a cache service operating on the node.
 2. Themethod of claim 1, wherein persisting a copy of each partitionidentified in the list of partitions to a persistence store withoutquiescing the cache service operating on the node comprises: generating,at the node, a snapshot task for each partition in the list ofpartitions in the snapshot request; and executing each snapshot task onthe node to persist said each partition wherein the node blockspersistent task on said each partition during executing of said eachpersistent task without quiescing the cache service operating on thenode.
 3. The method of claim 1, wherein persisting a copy of eachpartition identified in the list of partitions to a persistence storewithout quiescing the cache service operating on the node comprises:generating, at the node, a snapshot task for each partition in the listof partitions in the snapshot request; providing each snapshot task toan association pile on the node; executing each snapshot task on thenode to persist said each partition wherein the association pile blocksexecution of persistent tasks on said each partition during executing ofsaid each snapshot task without quiescing the cache service operating onthe node.
 4. The method of claim 1, wherein persisting a copy of eachpartition identified in the list of partitions to a persistence storewithout quiescing the cache service operating on the node comprises:generating, at the node, a snapshot task for each partition in the listof partitions in the snapshot request; providing each snapshot task toan association pile on the node; polling each snapshot task from theassociation pile using a thread of a thread pool on the node.
 5. Themethod of claim 1, wherein persisting a copy of each partitionidentified in the list of partitions to a persistence store withoutquiescing the cache service operating on the node comprises: generating,at the node, a snapshot task for each partition in the list ofpartitions in the snapshot request; providing each snapshot task to anassociation pile on the node; executing each snapshot task on a threadof a thread pool on the node to persist said each partition wherein theassociation pile blocks polling of persistent tasks on said eachpartition by said thread pool during executing of said each snapshottask without quiescing the cache service operating on the node.
 6. Themethod of claim 1, wherein persisting a copy of each partitionidentified in the list of partitions to a persistence store withoutquiescing the cache service operating on the node comprises: generating,at the node, a snapshot task for each partition in the list ofpartitions in the snapshot request; providing each snapshot task to anassociation pile on the node; executing each snapshot task on a threadof a scalable thread pool on the node to persist said each partitionwherein the association pile blocks polling of persistent tasks on saideach partition by said scalable thread pool during executing of saideach snapshot task without quiescing the cache service operating on thenode.
 7. The method of claim 1, further comprising: receiving at thesnapshot coordinator a response from each node indicative of a result ofpersisting a copy of each partition identified in the list of partitionsto a persistence store.
 8. The method of claim 1, further comprising:receiving at the snapshot coordinator a snapshot response from each nodeindicative of a result of persisting a copy of each partition identifiedin the list of partitions to a persistence store wherein the resultincludes success, failure, and duration information for persisting acopy of each partition.
 9. The method of claim 8, further comprising:transmitting another sending a snapshot request from a snapshotcoordinator to a node where persisting a copy of each partition failed.10. The method of claim 8, further comprising: monitoring snapshotresponse with the snapshot coordinator, and using data from saidsnapshot responses to modify further snapshot requests sent from thesnapshot coordinator.
 11. A system comprising a distributed data gridconfigured to perform partition-scoped snapshot creation wherein: anetworked plurality of computer systems each comprising a microprocessorand memory; a plurality of nodes operating on the plurality of computersystems; a snapshot coordinator operating on one of the plurality ofcomputer systems; a persistence store; wherein the distributed data thedistributed data grid is configured to perform steps comprising, sendinga snapshot request from a snapshot coordinator to a node storing aplurality of partitions, wherein the snapshot request includes asnapshot name and a list of partitions, receiving the snapshot requestat the node, and persisting a copy of each partition identified in thelist of partitions to the persistence store without quiescing a cacheservice operating on the node.
 12. The system of claim 11, whereinpersisting a copy of each partition identified in the list of partitionsto a persistence store without quiescing the cache service operating onthe node comprises: generating, at the node, a snapshot task for eachpartition in the list of partitions in the snapshot request; andexecuting each snapshot task on the node to persist said each partitionwherein the node blocks persistent task on said each partition duringexecuting of said each persistent task without quiescing the cacheservice operating on the node.
 13. The system of claim 11, whereinpersisting a copy of each partition identified in the list of partitionsto a persistence store without quiescing the cache service operating onthe node comprises: generating, at the node, a snapshot task for eachpartition in the list of partitions in the snapshot request; providingeach snapshot task to an association pile on the node; executing eachsnapshot task on the node to persist said each partition wherein theassociation pile blocks execution of persistent tasks on said eachpartition during executing of said each snapshot task without quiescingthe cache service operating on the node.
 14. The system of claim 11,wherein persisting a copy of each partition identified in the list ofpartitions to a persistence store without quiescing the cache serviceoperating on the node comprises: generating, at the node, a snapshottask for each partition in the list of partitions in the snapshotrequest; providing each snapshot task to an association pile on thenode; polling each snapshot task from the association pile using athread of a thread pool on the node.
 15. The system of claim 11, whereinpersisting a copy of each partition identified in the list of partitionsto a persistence store without quiescing the cache service operating onthe node comprises: generating, at the node, a snapshot task for eachpartition in the list of partitions in the snapshot request; providingeach snapshot task to an association pile on the node; executing eachsnapshot task on a thread of a thread pool on the node to persist saideach partition wherein the association pile blocks polling of persistenttasks on said each partition by said thread pool during executing ofsaid each snapshot task without quiescing the cache service operating onthe node.
 16. The system of claim 11, wherein persisting a copy of eachpartition identified in the list of partitions to a persistence storewithout quiescing the cache service operating on the node comprises:generating, at the node, a snapshot task for each partition in the listof partitions in the snapshot request; providing each snapshot task toan association pile on the node; executing each snapshot task on athread of a scalable thread pool on the node to persist said eachpartition wherein the association pile blocks polling of persistenttasks on said each partition by said scalable thread pool duringexecuting of said each snapshot task without quiescing the cache serviceoperating on the node.
 17. The system of claim 11, wherein thedistributed data is further configured to perform steps comprising:receiving at the snapshot coordinator a response from each nodeindicative of a result of persisting a copy of each partition identifiedin the list of partitions to a persistence store.
 18. The system ofclaim 11, wherein the distributed data is further configured to performsteps comprising: receiving at the snapshot coordinator a snapshotresponse from each node indicative of a result of persisting a copy ofeach partition identified in the list of partitions to a persistencestore wherein the result includes success, failure, and durationinformation for persisting a copy of each partition.
 19. The system ofclaim 18, wherein the distributed data is further configured to performsteps comprising: monitoring snapshot responses with the snapshotcoordinator, and using data from said snapshot responses to modifyfurther snapshot requests sent from the snapshot coordinator.
 20. Anon-transitory computer-readable storage medium including instructionsstored thereon for configuring a distributed data grid to supportpartition-scoped snapshot creation, which instructions, when executed,cause the distributed data grid to perform steps comprising: sending asnapshot request from a snapshot coordinator to a node storing aplurality of partitions, wherein the snapshot request includes asnapshot name and a list of partitions; receiving the snapshot requestat the node; and persisting a copy of each partition identified in thelist of partitions to a persistence store without quiescing a cacheservice operating on the node.